this post was submitted on 15 Feb 2024
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Twice for AI or computing in general?
Why does that make a difference? Compute for AI is build on the progress for compute first for GPU then for data center. They are similar in nature.
Yes they have exceeded 2x for AI for a while, but that has been achieved through exploding die size and cost, but even that won't make a million times faster in 10 years possible, because they can't increase die sizes any further.
Building an ASIC for purpose built computation is significantly faster than generic gpu compute cores. Like when ASICs were built for bitcoin mining/sha256 and a little 5 watt usb device could outperform the best GPUs
It may be even more specialized than that. It might be a return to analog computers.
Which isn't going to work for Nvidia's traditional products, either.
The H200 is evolved from Nvidia GPU designs, and will be by far the most powerful AI component in existence when it arrives later this year, AI is now so complex, that it doesn't really make sense to call it an ASIC or to use an ASIC for the purpose, and the cost is $40,000.- for a single H200 unit!!! So no not small 5 watt units, more like 100x that.
If they could make small ASICS that did the same, they'd all do it. Nvidia AMD Intel Google Amazon Huawei etc. But it's simply not an option.
Edit:
In principle the H200 AI/Compute system, is a giant cluster of tiny ASICS built onto one chip for massive parallel compute and greater speed.
There's also software improvements to consider, there's a lot of room for efficiency improvements.